Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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SYSTEM FOR ACTION DETERMINATION
FIELD OF THE INVENTION
The present invention relates to a system for action determination, and to a
method for
action determination, and to a computer program element and computer readable
medium.
BACKGROUND OF THE INVENTION
A process plant can have many process control systems, for example those used
in
chemical, petroleum and other industrial processes. One or more process
controllers
are communicatively coupled to various field devices such as valves, valve
positioners,
relays, switches, various sensors that monitor temperature, pressure,
position, flow
rates etc. The process controllers receive data signals indicative of process
measurements made by the field devices, which can be used to generate control
signals to implement control routines.
Users or operators in control rooms have access to information from the field
devices
and process controllers, and running appropriate software on computer systems
are
able to perform a variety of tasks, such as viewing the current state of the
process,
changing an operating state, changing settings of a process control routine,
modifying
the operation of the process controllers and/or the field devices.
Furthermore, such process plants have numerous alarm systems that monitor the
field
devices and process controllers. Alarm data is also provided to the users and
operators, and this forms an important aid identifying installation or process
states that
require immediate action. Since both individual components and subsystems of a
control system are designed to generate alarms. Thus tens of thousands of data
signals and alarm data can occur. However, if too many alarms are generated
during
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serious situations, the user/operator may possibly be confused if they are
inexperienced, and alarms which are actually important may remain unidentified
or
ignored in the flood of alarms or, an operator can take the wrong
decision/action or
make inconsistent actions. This is particularly problematic when an unusual
alarm or
event has just occurred or is occurring.
Historically, experienced operators have been able to deal with this
situation, but even
experienced operators with many years of experience have difficulties copying
with the
flood of alarms that can be experienced. As these experienced operators
retire, the
situation is exacerbated for junior or inexperienced operators.
There is a need to address this situation.
SUMMARY OF THE INVENTION
Therefore, it would be advantageous to have an improved ability to determine
actions
to be taken within such a process environment.
The object of the present invention is solved with the subject matter of the
independent
claims, wherein further embodiments are incorporated in the dependent claims.
In a first aspect, there is provided a system for action determination,
comprising:
an input unit;
a processing unit; and
an output unit.
The input unit is configured to provide the processing unit with information
relating to a
plurality of past actions over a period of time associated with the operation
of an
industrial process. The input unit is configured to provide the processing
unit with
information relating to a plurality of past process events over the time
period associated
with the operation of the industrial process. The input unit is configured to
provide the
processing unit with information relating to a new process event. The
processing unit is
configured to determine a correlation between at least some of the plurality
of past
actions with at least some of the past process events. The processing unit is
configured
to determine at least one recommended action from the information relating to
the new
process event, the determination comprising utilization of the determined
correlation.
The output unit is configured to output the at least one recommended action.
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I n the above, an action, whether a past action or a recommended action, is
anything
that can be logged. Thus, an action can be from an industrial processing
system. An
action can also be calling a specific telephone number. An action can be
opening a
specific application (for example: CMMS, emission monitoring, vibration
monitoring
etc). An action can be opening a specific document (for example a
pdf/html/VVord
document etc) on a specific page. An action can be messaging a specific person
or a
user group. An action can also be an "action group", in terms of being more
than one
action, and can for example be a sequence of multiple actions. Also, a past
action can
relate to an inspection activity associated with an industrial process.
In the above, the output unit can be part of the distributed control system
(DCS) itself,
where for example the output unit can be enabled to directly execute routine
actions
(for example the closing and/or opening of a valve). The output unit can be an
operator's console, where specific views (for example relevant alarm lists,
trend views,
process graphics) are opened to help the operator assess the situation and
take further
actions. The output unit can relate to an action outside of the DOS (for
example used to
call a colleague, create a work order, email colleague, call emergency
services etc).
In other words, a system is provided for determining actions based on past
actions and
events, such as alarms for an industrial process plant, that enables an
appropriate
action to be determined, when for example an unusual alarm or event arises and
enables a best course of action to remedy the situation to be determined.
Process plants can have many thousands of time varying data signals, with many
alarms and events occurring, often coming in floods of events. However, only a
few of
these signals, alarms and events are relevant to a specific problem in the
plant.
Operators, with years of experience can deal with this massive influx of
information to
determine the appropriate action in these situations, however as these people
retire it
is becoming ever harder for operators, especially junior operators, to take
the correct
action. Also, taking incorrect actions in situations with high risk is
expensive.
Sometimes, such junior operators act inconsistently, and take the wrong course
of
action. Thus, the developed system takes into account the influx of past
events, such
as alarms and signals, and takes into account how experienced operators have
acted
and processes all of the information to enable an appropriate action to be
determined
on the basis of a new process event, that none of the operators now present
have ever
witnessed before. But also, relatively mundane actions that the operator is
almost
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certain are correct, are confirmed by the system providing a "second pair of
eyes" in
situations when the operator knows what to do, but has that action confirmed.
Thus,
new operators can act more confidently and become more effective more quickly.
In an example, the information relating to the plurality of past process
actions
comprises one or more operator actions.
In an example, the information relating to the plurality of process actions
comprises one
or more actions on a GUI level.
In an example, the information relating to the plurality of process events
comprises one
or more alarms.
In an example, the information relating to the plurality of process events
comprises one
or more system states.
In an example, the one or more system states comprises one or more current
system
states and/or one or more predicted system states.
In an example, the new process event is an alarm.
In an example, the one or more alarms comprises a plurality of alarms and
wherein the
processing unit is configured to determine the at least some of the past
process events,
the determination comprising ignoring one or more alarms that appear multiple
times.
In an example, the one or more alarms comprises a plurality of alarms and
wherein the
processing unit is configured to determine the at least some of the past
process events,
the determination comprising ignoring one or more alarms that appear uniformly
over
the time period.
In an example, the one or more alarms comprises a plurality of alarms and
wherein the
processing unit is configured to determine the at least some of the past
process events.
The determination comprises clustering multiple identical alarms within a
defined short
time interval relative to the period of time into a single alarm.
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In an example, the one or more alarms comprises a plurality of alarms and
wherein
determination of the correlation comprises a statistical analysis to detect a
relationship
between alarms and actions.
In an example, the one or more alarms comprises a plurality of alarms and
wherein
determination of the correlation comprises a selection of alarms that occur
rarely during
the time period.
In an example, the one or more alarms comprises a plurality of alarms and
wherein
determination of the correlation comprises determination of an action to alarm
matrix.
In an example, determination of the correlation comprises a statistical
inversion of the
action to alarm matrix to determine a plurality of rules and associated
probabilities.
Determination of the at least one recommended action then comprises
utilization of the
plurality of rules and associated probabilities.
In an example, the processing unit is configured to remove one or more rules
that
correspond to an alarm that triggers a significantly high number of rules.
Determination
of the at least one recommended action then comprises utilization of the
plurality of
rules and associated probabilities that remain after removal of the one or
more rules.
In an example, the processing unit is configured to apply a principal
component
analysis to filter most significant rules. Determination of the at least one
recommended
action then comprises utilization of the most significant rules.
In an example, determination of the correlation comprises a utilization of
natural
language processing and word embeddings to obtain vectorial representations of
information relating to the plurality of past actions and the information
relating to the
plurality of past process events. Actions and events are determined to be
correlated
when related actions and events are mapped close together in feature space
with
respect to other mappings.
In an example, determination of the correlation comprises utilization of a
neural
network.
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In a second aspect, there is provided a method for action determination,
comprising:
a) providing a processing unit with information relating to a plurality of
past actions
over a period of time associated with the operation of an industrial process;
b) providing the processing unit with information relating to a plurality of
past
process events over the time period associated with the operation of the
industrial
process;
C) providing the processing unit with information relating to a new process
event;
d) determining by the processing unit a correlation between at least some of
the
plurality of past actions provided at step a) with at least some of the past
process
events provided at step b);
e) determining by the processing unit at least one recommended action from the
information relating to the new process event provided at step c), the
determination
comprising utilization of the correlation determined at step d); and
f) outputting by an output unit the at least one recommended action.
According to another aspect, there is provided a computer program element
controlling apparatus or system as previously described which, when the
computer
program element is executed by a processing unit, is adapted to perform the
method steps as previously described.
According to another aspect, there is also provided a computer readable medium
having stored the computer element as previously described.
The above aspects and examples will become apparent from and be elucidated
with
reference to the embodiments described hereinafter.
According to another aspect of the present invention, there is provided a
system for
action determination, comprising:
- an input unit;
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- a processing unit; and
- an output unit;
wherein, the input unit is configured to provide the processing unit with
information relating to a plurality of past actions over a period of time
associated
with the operation of an industrial process, wherein the information relating
to the
plurality of past actions comprises one or more operator actions;
wherein, the input unit is configured to provide the processing unit with
information relating to a plurality of past process events over the time
period
associated with the operation of the industrial process, wherein the
information
.. relating to the plurality of past process events comprises a plurality of
alarms,
wherein the processing unit is configured to ignore one or more alarms that
appear
uniformly over the time period, and wherein the processing unit is configured
to
cluster multiple identical alarms within a defined short time interval
relative to the
period of time into a single alarm;
wherein, the input unit is configured to provide the processing unit with
information relating to a new process event;
wherein, the processing unit is configured to determine a correlation between
the plurality of past actions with the past process events, wherein
determination of
the correlation comprises a statistical analysis to detect a relationship
between
alarms and actions, wherein determination of the correlation comprises
determination of an action to alarm matrix, wherein determination of the
correlation
comprises a statistical inversion of the action to alarm matrix to determine a
plurality
of rules and associated probabilities;
wherein the processing unit is configured to apply a principal component
analysis to filter most significant rules, and wherein filtering of the most
significant
rules comprises removal of one or more rules that correspond to an alarm that
triggers a significantly high number of rules;
wherein, the processing unit is configured to determine at least one
recommended action from the information relating to the new process event, the
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determination comprising utilization of the determined correlation, and
wherein
determination of the at least one recommended action comprises utilization of
the
most significant rules ordered by their associated probabilities; and
wherein, the output unit is configured to output the at least one recommended
action.
According to another aspect of the present invention, there is provided a
method for
action determination, comprising:
a) providing a processing unit with information relating to a plurality of
past
actions over a period of time associated with the operation of an industrial
process,
wherein the information relating to the plurality of past actions comprises
one or
more operator actions;
b) providing the processing unit with information relating to a plurality
of past
process events over the time period associated with the operation of the
industrial
process, wherein the information relating to the plurality of past process
events
comprises a plurality of alarms, wherein the processing unit is configured to
ignore
one or more alarms that appear uniformly over the time period, and wherein the
processing unit is configured to cluster multiple identical alarms within a
defined
short time interval relative to the period of time into a single alarm;
c) providing the processing unit with information relating to a new process
event;
d) determining by the processing unit a correlation between at least
some of the
plurality of past actions provided at step a) with at least some of the past
process
events provided at step b), wherein determination of the correlation comprises
a
statistical analysis to detect a relationship between alarms and actions,
wherein
determination of the correlation comprises determination of an action to alarm
matrix, wherein determination of the correlation comprises a statistical
inversion of
the action alarm matrix to determine a plurality of rules and associated
probabilities,
and wherein step d) comprises applying a principal component analysis to
filter most
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significant rules and wherein filtering of the most significant rules
comprises removal
of one or more rules that correspond to an alarm that triggers a significantly
high
number of rules;
e) determining by the processing unit at least one recommended action from
the
information relating to the new process event provided at step c), the
determination
comprising utilization of the correlation determined at step d), and wherein
step e)
comprises utilizing the most significant rules ordered by their associated
probabilities; and
f) outputting by an output unit the at least one recommended action.
BRIEF DESCRIPTION OF THE DRAWINGS
Exemplary embodiments will be described in the following with reference to the
following drawings:
Fig. 1 shows an example of a system and method for action determination within
a
process environment.
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DETAILED DESCRIPTION OF EMBODIMENTS
The present disclosure relates to a system and method for action
determination. In an
example, the system comprises an input unit, a processing unit, and an output
unit.
The input unit is configured to provide the processing unit with information
relating to a
plurality of past actions over a period of time associated with the operation
of an
industrial process. The input unit is configured also to provide the
processing unit with
information relating to a plurality of past process events over the time
period associated
io with the operation of the industrial process. The input unit is
configured also to provide
the processing unit with information relating to a new process event. The
processing
unit is configured to determine a correlation between at least some of the
plurality of
past actions with at least some of the past process events. The processing
unit is
configured also to determine at least one recommended action from the
information
relating to the new process event, the determination comprising utilization of
the
determined correlation. The output unit is configured to output the
recommended
action.
Thus, the output unit can in outputting one or more recommended actions
operate
automatically, and enable an action such as a valve being opened or closed to
occur
without human intervention. Or the output unit can output an action that
recommends
sending a technician to inspect a specific equipment based on a past record in
a work
management system. Or the output unit can output an action that recommends
calling
or alerting someone.
In an example, the processing unit is configured to implement a machine
learning
algorithm to determine the correlation between at least some of the plurality
of past
actions with at least some of the past process events
According to an example, the information relating to the plurality of past
process
actions comprises one or more operator actions.
According to an example, the information relating to the plurality of process
actions
comprises one or more actions on a GUI level.
In an example, an action at a GUI level comprises opening a trend.
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In an example, an action at a GUI level comprises opening a faceplate.
In an example, an action at a GUI level comprises changing a set-point.
In an example, an action at a GUI level comprises an action that is different
to a core
control action.
According to an example, the information relating to the plurality of process
events
comprises one or more alarms.
According to an example, the information relating to the plurality of process
events
comprises one or more system states.
According to an example, the one or more system states comprises one or more
current system states and/or one or more predicted system states.
According to an example, the new process event is an alarm.
According to an example, the one or more alarms comprises a plurality of
alarms. The
processing unit is configured to determine the at least some of the past
process events,
the determination comprising ignoring one or more alarms that appear multiple
times.
According to an example, the one or more alarms comprises a plurality of
alarms. The
processing unit is configured to determine the at least some of the past
process events,
the determination comprising ignoring one or more alarms that appear uniformly
over
the time period.
According to an example, the one or more alarms comprises a plurality of
alarms. The
processing unit is configured to determine the at least some of the past
process events,
the determination comprising clustering multiple identical alarms within a
defined short
time interval relative to the period of time into a single alarm.
According to an example, the one or more alarms comprises a plurality of
alarms and
determination of the correlation comprises a statistical analysis to detect a
relationship
between alarms and actions.
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According to an example, the one or more alarms comprises a plurality of
alarms and
determination of the correlation comprises a selection of alarms that occur
rarely during
the time period.
According to an example, the one or more alarms comprises a plurality of
alarms and
determination of the correlation comprises determination of an action to alarm
matrix.
According to an example, determination of the correlation comprises a
statistical
inversion of the action to alarm matrix to determine a plurality of rules and
associated
probabilities. Determination of the at least one recommended action comprises
utilization of the plurality of rules and associated probabilities.
According to an example, the processing unit is configured to remove one or
more
rules that correspond to an alarm that triggers a significantly high number of
rules.
Determination of the at least one recommended action comprises utilization of
the
plurality of rules and associated probabilities that remain after removal of
the one or
more rules.
According to an example, the processing unit is configured to apply a
principal
component analysis to filter most significant rules, and determination of the
at least one
recommended action comprises utilization of the most significant rules.
According to an example, determination of the correlation comprises a
utilization of
natural language processing and word embeddings to obtain vectorial
representations
of information relating to the plurality of past actions and the information
relating to the
plurality of past process events. Actions and events are determined to be
correlated
when related actions and events are mapped close together in feature space
with
respect to other mappings.
According to an example, determination of the correlation comprises
utilization of a
neural network.
Thus, associated with the system is a method for action determination,
comprising:
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a) providing a processing unit with information relating
to a plurality of past actions over a period of time associated with the
operation of an
industrial process;
b) providing the processing unit with information relating
to a plurality of past process events over the time period associated with the
operation
of the industrial process;
C) providing the processing unit with
information relating
to a new process event;
d) determining by the processing unit a correlation
io between at least some of the plurality of past actions provided at step
a) with at least
some of the past process events provided at step b);
e) determining by the processing unit at least one
recommended action from the information relating to the new process event
provided
at step c), the determination comprising utilization of the correlation
determined at step
d); and
f) outputting by an output unit the at least one
recommended action.
In an example, in step a) the information relating to the plurality of past
process actions
comprises one or more operator actions.
In an example, in step a) the information relating to the plurality of process
actions
comprises one or more actions on a GUI level.
In an example, an action at a GUI level comprises opening a trend.
In an example, an action at a GUI level comprises opening a faceplate.
In an example, an action at a GUI level comprises changing a set-point.
In an example, an action at a GUI level comprises an action that is different
to a core
control action.
In an example, in step b) the information relating to the plurality of process
events
comprises one or more alarms.
In an example, in step b) the information relating to the plurality of process
events
comprises one or more system states.
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I n an example the one or more system states comprises one or more current
system
states and/or one or more predicted system states.
In an example, in step c) the information relating to a new process event
relates to a
process event that is an alarm.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein in step d) the processing unit determines the at least some
of the
past process events, the determination comprising ignoring one or more alarms
that
appear multiple times.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein in step d) the processing unit determines the at least some
of the
past process events, the determination comprising ignoring one or more alarms
that
appear uniformly over the time period.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein in step d) the processing unit determines the at least some
of the
past process events, the determination comprising clustering multiple
identical alarms
within a defined short time interval relative to the period of time into a
single alarm.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein step d) comprises a statistical analysis to detect a
relationship
between alarms and actions.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein step d) selecting alarms that occur rarely during the time
period.
In an example, in step b) the plurality of past process events comprises a
plurality of
alarms and wherein step d) determining an action to alarm matrix.
In an example, step d) comprises determining a statistical inversion of the
action to
alarm matrix to determine a plurality of rules and associated probabilities,
and wherein
step e) comprises utilizing the plurality of rules and associated
probabilities.
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I n an example, step d) comprises removing one or more rules that correspond
to an
alarm that triggers a significantly high number of rules, and wherein step e)
comprises
utilizing the plurality of rules and associated probabilities that remain
after removal of
the one or more rules.
In an example, step d) comprises applying a principal component analysis to
filter most
significant rules, and wherein step e) comprises utilizing the most
significant rules.
In an example, step d) comprises utilizing natural language processing and
word
embeddings to obtain vectorial representations of information relating to the
plurality of
past actions and the information relating to the plurality of past process
events, and
wherein actions and events are determined to be correlated when related
actions and
events are mapped close together in feature space with respect to other
mappings.
In an example, step d) comprises utilizing a machine learning algorithm, such
as a
neural network or decision tree algorithm.
Thus, in the new system and method for action determination operator actions
are
captured and correlated with process events (e.g., alarms, current system
state or
predicted system state). Actions on GUI level are also captured (opening a
trend,
opening a faceplate), thus the correlated actions are not only core control
actions. Then
recommendation of actions can be made, when similar process event occurs
again. In
this way a foundation is built for future fully autonomous process control
(i.e., an
Artificial Intelligence based system that can learn from operators how to
respond to
abnormal conditions and prevent them).
To put another way, operator actions on events (e.g., alarms) are captured for
training
purposes, providing for a continuous and affordable solution for alarm
rationalization.
This also enables operator efficiency to be increased by allowing to "replay"
past
actions, and build up a knowledge base for Al-based operation. In this manner,
the
system gradually keeps improving with respect to the suggestions (recommended
actions). This can be augmented through for example manual feedback, for
example
through rationalization of past process events and past actions, facilitated
by the
captured historical actions and events.
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Thus, previous problems have been that plant operators receive too many
alarms, and
where important alarms might be ignored. Know-How of experienced operators
becomes lost, and there may be insufficient training for junior operators, and
no best
practices for how to react to specific alarms. Reactions to alarms are time
consuming
and inconsistent, and rationalization is very expensive, and often alarms have
never
been rationalized. That means that most of the alarm occurrences are
meaningless. In
addition, important alarms might have been overlooked, i.e. no action is
performed,
although it would have been required. These are additional obstacles for an
analysis,
because many statistical approaches will not work under such conditions.
The system and method for action determination described here addresses these
issues.
The system and method for action determination, in a very detailed embodiment
as
exemplified by Fig. 1, includes the following:
Data Logging: Record both alarms and operator actions;
Preprocessing: Remove insignificant frequent items;
Rule Detection Algorithm: Learn relationship between alarms and actions;
Post-processing: Remove rules that occur by coincidence;
Action suggestion and Rationalization: Interface to accept/modify/reject
detected
rules
Data Logging
In addition to alarm logging record all operator actions like open face plate,
open trend,
change set-point etc with all significant parameters are recorded, so that
actions would
be reproducible from logged data. A format is used for storing, that can
easily be
reused for redoing the action (e.g. open a trend automatically whenever the
alarm
occurs).
Preprocessing
Insignificant and very frequent alarms are removed by ignoring alarms that
appear
uniformly with high frequency;
Multiple identical alarms, within defined short time frames, are clustered
into one alarm.
Rule Detection
Statistical Analysis is used to detect the relationship between alarms and
actions. A
focus is taken on rare and very rare alarms (e.g. occur less than once a
week/month/year);
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An Action to Alarm Matrix is set up: For every action taken and every alarm,
it is
computed how often that alarm occurred in a defined time-span before the
action. The
Action to Alarm Matrix is statistically inverted to find possible rule
candidates and
probabilities.
Beyond statistical analysis: Natural Language Processing and Word Embeddings
is
used to obtain vectorial representations of alarms and events in such a way
that related
alarms / events are mapped to close points in feature space. In this way know-
how can
be transferred between identical sections/equipment (.e.g. several identical
coal
mills).Also the relation between closely related sensors, like redundant
sensors, can be
taken into account (current approaches treat each alarm separately as soon as
the
name is different);
A prediction model such as neuronal network is taught for alarm/action
relationship
based on the word embeddings.
Post-processing
Principal Component Analysis is applied (Low rank approximation via singular
value
decomposition) to the Action to Alarm Matrix to filter most significant rules;
Rules are removed that occur by coincidence: Rules are removed that correspond
to
an alarm that triggers many rules and these examples for these rules are
correlated by
time in historical data.
Action suggestion and Rationalization
Whenever an alarm occurs, actions are suggested to the operator corresponding
to
detected rules, ordered by probability.
Operators and/or experts are given the possibility to rationalize a detected
rule. This is
provided because some of the rules might be wrong (e.g. based on the actions
of a
junior operator who repeatedly made the same mistake);
Abnormal Situations (hopefully) don't happen every day. Therefore log files
are needed
for long periods >> 1 year to catch enough interesting abnormal situations.
(Many
industrial alarm log systems delete alarms after a certain period, often less
than a
year);
Nuisance alarms (alarms with a high frequency) could destroy the analysis, and
the
system and method deals with this.
In another exemplary embodiment, a computer program or computer program
element
is provided that is characterized by being configured to execute the method
steps of
the method according to one of the preceding embodiments, on an appropriate
system.
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WO 2020/216718 PCT/EP2020/061008
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The computer program element might therefore be stored on a computer unit,
which
might also be part of an embodiment. This computing unit may be configured to
perform or induce performing of the steps of the method described above.
Moreover, it
may be configured to operate the components of the above described apparatus
and/or
system. The computing unit can be configured to operate automatically and/or
to
execute the orders of a user. A computer program may be loaded into a working
memory of a data processor. The data processor may thus be equipped to carry
out
the method according to one of the preceding embodiments.
According to a further exemplary embodiment of the present invention, a
computer
readable medium, such as a CD-ROM, is presented wherein the computer readable
medium has a computer program element stored on it which computer program
element is described by the preceding section.
While the invention has been illustrated and described in detail in the
drawings and
foregoing description, such illustration and description are to be considered
illustrative
or exemplary and not restrictive. The invention is not limited to the
disclosed
embodiments. Other variations to the disclosed embodiments can be understood
and
effected by those skilled in the art in practicing a claimed invention, from a
study of the
drawings, the disclosure, and the dependent claims.
